A scalable PQ event identification system

Author

Santoso, Surya ; Lamoree, Jeff ; Grady, W. Mack ; Powers, Edward J. ; Bhatt, Siddharth C.

Author_Institution

Electrotek Concepts Inc., Knoxville, TN, USA

Volume

15

Issue

2

fYear

2000

fDate

4/1/2000 12:00:00 AM

Firstpage

738

Lastpage

743

Abstract

A scalable event identification system for power quality events is proposed. Unlike ANN-based approaches where the system is not scalable and not “debug-able” without retraining, the proposed approach is particularly advantageous compared to those of ANN´s since it is scalable, debug-able and easily modified. This approach is adopted from artificial intelligence´s rule-based approach and attempts to mimic power engineers thought process in identifying PQ events. This paper describes prerequisites in constructing such a scalable system. Examples of rules to identify power quality event are also presented. The prototype of the system is built and tested using 770 field-measured voltage waveforms which covers ten types of PQ events. The accuracy rate is nearly 95% with less than 6% of rejection rate. Potential applications of the proposed system in PQ community are also described

Keywords

knowledge based systems; learning (artificial intelligence); neural nets; power supply quality; power system analysis computing; power system identification; accuracy; artificial intelligence; artificial neural nets; computer simulation; field-measured voltage waveforms; power quality events; rule-based approach; scalable PQ event identification system; Artificial intelligence; Artificial neural networks; Condition monitoring; Design engineering; Least squares methods; Neurons; Power engineering and energy; Power quality; Prototypes; Remote monitoring;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/61.853013

Filename

853013